• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 1667680
  • 313057
  • 10220
  • 6567
  • 1227
  • 874
  • 182
  • 181
  • 180
  • 176
  • 167
  • 162
  • 139
  • 129
  • 59
  • Tagged with
  • 132590
  • 77323
  • 72887
  • 66614
  • 63660
  • 55173
  • 49126
  • 47602
  • 45573
  • 41193
  • 36013
  • 34476
  • 33801
  • 32112
  • 31294
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1609541

Proactive Identification of Cybersecurity Threats Using Online Sources

January 2019 (has links)
abstract: Many existing applications of machine learning (ML) to cybersecurity are focused on detecting malicious activity already present in an enterprise. However, recent high-profile cyberattacks proved that certain threats could have been avoided. The speed of contemporary attacks along with the high costs of remediation incentivizes avoidance over response. Yet, avoidance implies the ability to predict - a notoriously difficult task due to high rates of false positives, difficulty in finding data that is indicative of future events, and the unexplainable results from machine learning algorithms. In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to support prioritizing defense measures. The first two approaches leverage ML on cyberthreat intelligence data to predict if exploits are going to be used in the wild. The first work focuses on what data feeds are generated after vulnerability disclosures. The developed ML models outperform the current industry-standard method with F1 score more than doubled. Then, an approach to derive features about who generated the said data feeds is developed. The addition of these features increase recall by over 19% while maintaining precision. Finally, frequent itemset mining is combined with a variant of a probabilistic temporal logic framework to predict when attacks are likely to occur. In this approach, rules correlating malicious activity in the hacking community platforms with real-world cyberattacks are mined. They are then used in a deductive reasoning approach to generate predictions. The developed approach predicted unseen real-world attacks with an average increase in the value of F1 score by over 45%, compared to a baseline approach. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019
1609542

Assessing Experiential Learning in Construction Education by Modeling Student Performance

January 2019 (has links)
abstract: The typical engineering curriculum has become less effective in training construction professionals because of the evolving construction industry needs. The latest National Science Foundation and the National Academies report indicate that industry-valued skills are changing. The Associated General Contractors of America recently stated that contractors expect growth in all sectors; however, companies are worried about the supply of skilled professionals. Workforce development has been of a growing interest in the construction industry, and this study approaches it by conducting an exploratory analysis applied to students that have completed a mandatory internship as part of their construction program at Arizona State University, in the School of Sustainable Engineering and the Built Environment. Data is collected from surveys, including grades by a direct evaluator from the company reflecting each student’s performance based on recent Student Learning Objectives. Preliminary correlations are computed between scores received on the 15 metrics in the survey and the final industry suggested grade. Based on the factors identified as highest predictors: ingenuity and creativity, punctuality and attendance, and initiative; a prognostic model of student performance in the construction industry is generated. With regard to graduate employability, student performance in the industry and human predispositions are also tested in order to evaluate their contribution to the generated model. The study finally identifies threats to validity and opportunities presented in a dynamic learning environment presented by internships. Results indicate that measuring student performance during internships in the construction industry creates challenges for the evaluator from the host company. Scoring definitions are introduced to standardize the evaluators’ grading based on observations of student behavior. 12 questions covering more Student Learning Objectives identified by the industry are added to the survey, potentially improving the reliability of the predictive model. / Dissertation/Thesis / Doctoral Dissertation Construction Management 2019
1609543

Soil Microbial Responses to Different Precipitation Regimes Across a Southwestern United States Elevation Gradient

January 2019 (has links)
abstract: Soil organic carbon (SOC) is a critical component of the global carbon (C) cycle, accounting for more C than the biotic and atmospheric pools combined. Microbes play an important role in soil C cycling, with abiotic conditions such as soil moisture and temperature governing microbial activity and subsequent soil C processes. Predictions for future climate include warmer temperatures and altered precipitation regimes, suggesting impacts on future soil C cycling. However, it is uncertain how soil microbial communities and subsequent soil organic carbon pools will respond to these changes, particularly in dryland ecosystems. A knowledge gap exists in soil microbial community responses to short- versus long-term precipitation alteration in dryland systems. Assessing soil C cycle processes and microbial community responses under current and altered precipitation patterns will aid in understanding how C pools and cycling might be altered by climate change. This study investigates how soil microbial communities are influenced by established climate regimes and extreme changes in short-term precipitation patterns across a 1000 m elevation gradient in northern Arizona, where precipitation increases with elevation. Precipitation was manipulated (50% addition and 50% exclusion of ambient rainfall) for two summer rainy seasons at five sites across the elevation gradient. In situ and ex situ soil CO2 flux, microbial biomass C, extracellular enzyme activity, and SOC were measured in precipitation treatments in all sites. Soil CO2 flux, microbial biomass C, extracellular enzyme activity, and SOC were highest at the three highest elevation sites compared to the two lowest elevation sites. Within sites, precipitation treatments did not change microbial biomass C, extracellular enzyme activity, and SOC. Soil CO2 flux was greater under precipitation addition treatments than exclusion treatments at both the highest elevation site and second lowest elevation site. Ex situ respiration differed among the precipitation treatments only at the lowest elevation site, where respiration was enhanced in the precipitation addition plots. These results suggest soil C cycling will respond to long-term changes in precipitation, but pools and fluxes of carbon will likely show site-specific sensitivities to short-term precipitation patterns that are also expected with climate change. / Dissertation/Thesis / Masters Thesis Biology 2019
1609544

Time-Domain/Digital Frequency Synchronized Hysteresis Based Fully Integrated Voltage Regulator

January 2019 (has links)
abstract: Power management integrated circuit (PMIC) design is a key module in almost all electronics around us such as Phones, Tablets, Computers, Laptop, Electric vehicles, etc. The on-chip loads such as microprocessors cores, memories, Analog/RF, etc. requires multiple supply voltage domains. Providing these supply voltages from off-chip voltage regulators will increase the overall system cost and limits the performance due to the board and package parasitics. Therefore, an on-chip fully integrated voltage regulator (FIVR) is required. The dissertation presents a topology for a fully integrated power stage in a DC-DC buck converter achieving a high-power density and a time-domain hysteresis based highly integrated buck converter. A multi-phase time-domain comparator is proposed in this work for implementing the hysteresis control, thereby achieving a process scaling friendly highly digital design. A higher-order LC notch filter along with a flying capacitor which couples the input and output voltage ripple is implemented. The power stage operates at 500 MHz and can deliver a maximum power of 1.0 W and load current of 1.67 A, while occupying 1.21 mm2 active die area. Thus achieving a power density of 0.867 W/mm2 and current density of 1.377 A/mm2. The peak efficiency obtained is 71% at 780 mA of load current. The power stage with the additional off-chip LC is utilized to design a highly integrated current mode hysteretic buck converter operating at 180 MHz. It achieves 20 ns of settling and 2-5 ns of rise/fall time for reference tracking. The second part of the dissertation discusses an integrated low voltage switched-capacitor based power sensor, to measure the output power of a DC-DC boost converter. This approach results in a lower complexity, area, power consumption, and a lower component count for the overall PV MPPT system. Designed in a 180 nm CMOS process, the circuit can operate with a supply voltage of 1.8 V. It achieves a power sense accuracy of 7.6%, occupies a die area of 0.0519 mm2, and consumes 0.748 mW of power. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019
1609545

Nanoscale Electronic Properties in GaN Based Structures for Power Electronics Using Electron Microscopy

January 2019 (has links)
abstract: The availability of bulk gallium nitride (GaN) substrates has generated great interest in the development of vertical GaN-on-GaN power devices. The vertical devices made of GaN have not been able to reach their true potential due to material growth related issues. Power devices typically have patterned p-n, and p-i junctions in lateral, and vertical direction relative to the substrate. Identifying the variations from the intended layer design is crucial for failure analysis of the devices. A most commonly used dopant profiling technique, secondary ion mass spectroscopy (SIMS), does not have the spatial resolution to identify the dopant distribution in patterned devices. The possibility of quantitative dopant profiling at a sub-micron scale for GaN in a scanning electron microscope (SEM) is discussed. The total electron yield in an SEM is shown to be a function of dopant concentration which can potentially be used for quantitative dopant profiling. Etch-and-regrowth is a commonly employed strategy to generate the desired patterned p-n and p-i junctions. The devices involving etch-and-regrowth have poor performance characteristics like high leakage currents, and lower breakdown voltages. This is due to damage induced by the dry etching process, and the nature of the regrowth interface, which is important to understand in order to address the key issue of leakage currents in etched and regrown devices. Electron holography is used for electrostatic potential profiling across the regrowth interfaces to identify the charges introduced by the etching process. SIMS is used to identify the impurities introduced at the interfaces due to etch-and-regrowth process. / Dissertation/Thesis / Doctoral Dissertation Materials Science and Engineering 2019
1609546

Highly Multiplexed Single Cell In Situ Transcriptomic Analysis

January 2019 (has links)
abstract: Spatial resolved detection and quantification of ribonucleic acid (RNA) molecules in single cell is crucial for the understanding of inherent biological issues, like mechanism of gene regulation or the development and maintenance of cell fate. Conventional methods for single cell RNA profiling, like single-cell RNA sequencing (scRNA-seq) or single-molecule fluorescent in situ hybridization (smFISH), suffer either from the loss of spatial information or the low detection throughput. In order to advance single-cell analysis, new approaches need to be developed with the ability to perform high-throughput detection while preserving spatial information of the subcellular location of target RNA molecules. Novel approaches for highly multiplexed single cell in situ transcriptomic analysis were developed by our group to enable single-cell comprehensive RNA profiling in their native spatial contexts. Reiterative FISH was demonstrated to be able to detect >100 RNA species in single cell in situ, while more sophisticated approaches, consecutive FISH (C-FISH) and switchable fluorescent oligonucleotide based FISH (SFO-FISH), have the potential for whole transcriptome profiling at the single molecule sensitivity. The introduction of a cleavable fluorescent tyramide even enables sensitive RNA profiling in intact tissues with high throughput. These approaches will have wide applications in studies of systems biology, molecular diagnosis and targeted therapies. / Dissertation/Thesis / Doctoral Dissertation Chemistry 2019
1609547

An Ethnography of the Living's Solidarity with the Dead Tibetan Refugees and Their Self-Immolators

January 2019 (has links)
abstract: Since 1998 and as recently as November 2018, 165 Tibetans have burned themselves alive in public protest, both inside Tibet and in exile. This study foregrounds Tibetan refugees’ interpretations of the self-immolation protests and examines how the exile community has socially, politically, and emotionally interrogated and assimilated this resistance movement. Based upon eleven months of ethnographic field research and 150 hours of formal interviews with different groups of Tibetan refugees in northern India, including: freedom activists, former political prisoners, members of the exile parliament, teachers of Tibetan Buddhism, families of self-immolators, and survivors of self-immolation, this project asks: What does activism look like in a time of martyrdom? What are the practices of solidarity with the dead? How does a refugee community that has been in exile for over three generations make sense of a wave of death occurring in a homeland most cannot access? Does the tactic of self-immolation challenge Tibetan held conceptions of resistance and the conceived relationship between politics, religion and nation? These questions are examined with attention to the sociopolitical expectations and vulnerabilities that the refugee community face. This study thus analyzes what it means to mourn those one never knew, and examines the fractious connections between resistance, solidarity, trauma, representation, political exigency, and community cohesion. By examining the uncomfortable affect around self-immolation, its memorialization and representation, the author argues that self-immolation is a relational act that creates and ushers forth witnesses. As such, one must analyze the obligations of witnessing, the barriers to witnessing, and the expectations of solidarity. This project offers the theory of exigent solidarity, whereby solidarity is understood as a contested space, borne of expectation, pressure, and responsibility, with its expression complex and its execution seemingly impossible. It calls for attention to the affective labor of solidarity in a time of ongoing martyrdom, and demonstrates that in the need to maintain solidarity and social cohesion, a sense of mutual-becoming occurs whereby the community is reconciled uneasily into a shared fate. / Dissertation/Thesis / Doctoral Dissertation Justice Studies 2019
1609548

Explainable AI in Workflow Development and Verification Using Pi-Calculus

January 2020 (has links)
abstract: Computer science education is an increasingly vital area of study with various challenges that increase the difficulty level for new students resulting in higher attrition rates. As part of an effort to resolve this issue, a new visual programming language environment was developed for this research, the Visual IoT and Robotics Programming Language Environment (VIPLE). VIPLE is based on computational thinking and flowchart, which reduces the needs of memorization of detailed syntax in text-based programming languages. VIPLE has been used at Arizona State University (ASU) in multiple years and sections of FSE100 as well as in universities worldwide. Another major issue with teaching large programming classes is the potential lack of qualified teaching assistants to grade and offer insight to a student’s programs at a level beyond output analysis. In this dissertation, I propose a novel framework for performing semantic autograding, which analyzes student programs at a semantic level to help students learn with additional and systematic help. A general autograder is not practical for general programming languages, due to the flexibility of semantics. A practical autograder is possible in VIPLE, because of its simplified syntax and restricted options of semantics. The design of this autograder is based on the concept of theorem provers. To achieve this goal, I employ a modified version of Pi-Calculus to represent VIPLE programs and Hoare Logic to formalize program requirements. By building on the inference rules of Pi-Calculus and Hoare Logic, I am able to construct a theorem prover that can perform automated semantic analysis. Furthermore, building on this theorem prover enables me to develop a self-learning algorithm that can learn the conditions for a program’s correctness according to a given solution program. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020
1609549

Aerodynamic Characterization of a Tethered Rotor

January 2019 (has links)
abstract: An airborne, tethered, multi-rotor wind turbine, effectively a rotorcraft kite, provides one platform for accessing the energy in high altitude winds. The craft is maintained at altitude by its rotors operating in autorotation, and its equilibrium attitude and dynamic performance are affected by the aerodynamic rotor forces, which in turn are affected by the orientation and motion of the craft. The aerodynamic performance of such rotors can vary significantly depending on orientation, influencing the efficiency of the system. This thesis analyzes the aerodynamic performance of an autorotating rotor through a range of angles of attack covering those experienced by a typical autogyro through that of a horizontal-axis wind turbine. To study the behavior of such rotors, an analytical model using the blade element theory coupled with momentum theory was developed. The model uses a rigid-rotor assumption and is nominally limited to cases of small induced inflow angle and constant induced velocity. The model allows for linear twist. In order to validate the model, several rotors -- off-the-shelf model-aircraft propellers -- were tested in a low speed wind tunnel. Custom built mounts allowed rotor angles of attack from 0 to 90 degrees in the test section, providing data for lift, drag, thrust, horizontal force, and angular velocity. Experimental results showed increasing thrust and angular velocity with rising pitch angles, whereas the in-plane horizontal force peaked and dropped after a certain value. The analytical results revealed a disagreement with the experimental trends, especially at high pitch angles. The discrepancy was attributed to the rotor operating in turbulent wake and vortex ring states at high pitch angles, where momentum theory has proven to be invalid. Also, aerodynamic design constants, which are not precisely known for the test propellers, have an underlying effect on the analytical model. The developments of the thesis suggest that a different analytical model may be needed for high rotor angles of attack. However, adding a term for resisting torque to the model gives analytical results that are similar to the experimental values. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2019
1609550

Viewpoint Recommendation for Aesthetic Photography

January 2019 (has links)
abstract: This thesis addresses the problem of recommending a viewpoint for aesthetic photography. Viewpoint recommendation is suggesting the best camera pose to capture a visually pleasing photograph of the subject of interest by using any end-user device such as drone, mobile robot or smartphone. Solving this problem enables to capture visually pleasing photographs autonomously in areal photography, wildlife photography, landscape photography or in personal photography. The viewpoint recommendation problem can be divided into two stages: (a) generating a set of dense novel views based on the basis views captured about the subject. The dense novel views are useful to better understand the scene and to know how the subject looks from different viewpoints and (b) each novel is scored based on how aesthetically good it is. The viewpoint with the greatest aesthetic score is recommended for capturing a visually pleasing photograph. / Dissertation/Thesis / Masters Thesis Computer Engineering 2019

Page generated in 1.7447 seconds